Surrogate modelling of solar radiation potential for the design of PV module layout on entire façade of tall buildings
Bibliographic record
Abstract
This research investigated the performance of a surrogate modeling approach for the simulation of solar radiation potential on the vertical surfaces of tall buildings. Surrogate modeling is used to approximate the input–output behavior of the existing simulation model. The Random Forest (RF) machine learning approach was used to investigate three different scenarios, namely (1) Random variation, (2) Grid variation, and (3) Uniform variation, and the Genetic Algorithm is used to optimize the hyperparameters. A case study was performed to investigate the performance of surrogate models using a building in the Sir George William (SGW) campus of Concordia University in downtown Montreal Canada. The results suggest that even by only using a small sample size of the random solutions, surrogate modeling can achieve up to 94% accuracy in the prediction of solar radiation potentials. From the three scenarios, the best accuracy was obtained when using the Random variation method. In short, solar radiation simulation is very complex and too sensitive to the location and shadow effect. Therefore, simplification of those factors cannot be made to approximate the solar radiation potential. Also, using RF, the computational time improved by 16 times faster than when using the existing simulation model.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".